We implemented 4 VDCNN architectures with different depths: 9, 17, 29 and 49. Every architecture starts with an Embedding layer, followed by a 1D Conv layer with input size equal to embedding size and output size of 64. Then, they have a set of Convolution Blocks. The number of Convolution Blocks depends on the depth of the architecture, but can be seen in [1]}-Table 2. After the Convolution Blocks, comes a K-Max Pooling layer, a Linear layer with input of \(512*k\) , where \(k\)  is the number selected for the pooling layer, \((2)\) , and output of 2048. Following it, a Linear Layer with 2048 as input and output is inserted and finally, a Linear Layer with an input size of 2048, outputs the classification vector.
